---
title: "How Bad is COVID Now?"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
source_code: embed
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(plotly)
```
```{r, message=F, echo=F, warning=FALSE}
library(RColorBrewer)
library(reshape2)
library(tidyverse)
library(tidyquant)
library(ggplot2)
library(plotly)
library(riingo)
library(quantmod)
require("quantmod")
library(writexl)
library(scales)
library(plotly)
library(plyr)
confirmed_world <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
stringsAsFactors = FALSE, check.names = FALSE)
death_world <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv",
stringsAsFactors = FALSE, check.names = FALSE)
recovered_world <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv",
stringsAsFactors = FALSE, check.names = FALSE)
confirmed_world <- confirmed_world[,-c(5:(ncol(confirmed_world)-120))]
death_world <- death_world[,-c(5:(ncol(death_world)-120))]
recovered_world <- recovered_world[,-c(5:(ncol(recovered_world)-120))]
confirmed_world <- melt(confirmed_world, id.vars = c("Province/State", "Country/Region", "Lat", "Long"),
variable.name = "Date", value.name = "Confirmed")
death_world <- melt(death_world, id.vars = c("Province/State", "Country/Region", "Lat", "Long"),
variable.name = "Date", value.name = "Death")
recovered_world <- melt(recovered_world, id.vars = c("Province/State", "Country/Region", "Lat", "Long"),
variable.name = "Date", value.name = "Recovered")
combined <- sort(union(levels(confirmed_world$Date), levels(death_world$Date)))
combined <- sort(union(combined, levels(recovered_world$Date)))
confirmed_world <- mutate(confirmed_world, Date= factor(Date, levels= combined))
death_world <- mutate(death_world, Date= factor(Date, levels= combined))
recovered_world <- mutate(recovered_world, Date= factor(Date, levels= combined))
world_history_data <- left_join(confirmed_world, death_world,
by= c("Province/State", "Country/Region", "Lat", "Long", "Date"))
world_history_data <- left_join(world_history_data, recovered_world,
by= c("Province/State", "Country/Region", "Lat", "Long", "Date"))
world_history_data$Date <- as.Date(as.character(world_history_data$Date), format = c("%m/%d/%y"))
colnames(world_history_data) <- make.names(colnames(world_history_data))
coronavirus <- world_history_data
lastday <- max(coronavirus$Date)
cols <- matrix(c(brewer.pal(9,"Set1"),brewer.pal(11,"Set3")),ncol=1)
world.summary.data <- ddply(world_history_data,.(Country.Region, Date),function(x){
colSums(x[,c("Confirmed","Death","Recovered")])
})
world.summary.data <- world.summary.data[world.summary.data$Date<=lastday,]
yesterday.data <- world.summary.data[world.summary.data$Date==lastday,]
sort.index <- sort(yesterday.data$Confirmed,decreasing=TRUE,index.return=TRUE)$ix
yesterday.data.major <- yesterday.data[sort.index[1:20],]
yesterday.data.major <- data.frame(Country.Region=yesterday.data$Country.Region[sort.index[1:20]])
yesterday.data.major$Country.Region <- as.character(yesterday.data.major$Country.Region )
major.summary.data <- dplyr::inner_join(world.summary.data,yesterday.data.major,by = "Country.Region")
rownames(cols) <- unique(major.summary.data$Country.Region)
major.summary.data$Country.Region <- factor(major.summary.data$Country.Region,
levels = rev(yesterday.data.major$Country.Region))
attach(major.summary.data)
recentdate <- major.summary.data$Date[nrow(major.summary.data)]
recentdate_countries <- major.summary.data[(major.summary.data$Date == recentdate),]
topcountries <- as.character(recentdate_countries[order(-recentdate_countries$Confirmed),1])
## Plotting only ONE country's data
countries <- function(Country = ct){
country <- Country
major.summary.data_countries <- subset(major.summary.data, subset = Country.Region %in% country)
major.summary.data_countries$Diff[2:nrow(major.summary.data_countries)] <- diff(major.summary.data_countries$Confirmed)
major.summary.data_countries$Diff[1] <- 0
a <- list(
x = major.summary.data_countries$Date[1],
y = max(major.summary.data_countries$Diff),
text = major.summary.data_countries[(major.summary.data_countries$Date == recentdate),"Confirmed"],
xref = "x",
yref = "y",
showarrow=FALSE
)
plot_ly(major.summary.data_countries,
x=~Date, y=~Diff, type = "bar") %>%
layout(xaxis=list(title="Date", tickfont = list(size = 9)), yaxis=list(title="Cases")) %>%
layout(title = list(text = paste("Daily New Confirmed Cases for", country, sep=" "))) %>%
layout(yaxis = list(range = c(0, 1.1*(max(major.summary.data_countries$Diff)))))%>%
config(displayModeBar = F)
}
```
# Daily Confirmed Cases in Top Countries {.tabset .tabset-fade}
Row
-------------------------------------
###
```{r, message=F, echo=FALSE, warning=FALSE}
countries(topcountries[1])
```
###
```{r, message=F, echo=FALSE, warning=FALSE}
countries(topcountries[2])
```
Row
-------------------------------------
###
```{r, message=F, echo=FALSE, warning=FALSE}
countries(topcountries[3])
```
###
```{r, message=F, echo=FALSE, warning=FALSE}
countries(topcountries[4])
```
Row
-------------------------------------
###
```{r, message=F, echo=FALSE, warning=FALSE}
countries(topcountries[5])
```
###
```{r, message=F, echo=FALSE, warning=FALSE}
countries(topcountries[6])
```
```{r, message=F, echo=FALSE, warning=FALSE}
library(RColorBrewer)
library(reshape2)
library(tidyverse)
library(tidyquant)
library(ggplot2)
library(plotly)
library(riingo)
library(quantmod)
require("quantmod")
library(writexl)
library(scales)
library(plotly)
library(plyr)
library(htmltab)
library(tibble)
library(taRifx)
confirmed_US <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv",
stringsAsFactors = FALSE, check.names = FALSE)
confirmed_US <- confirmed_US[,c(-1,-2,-3,-4,-5,-6,-8,-9,-10,-11)]
death_US <- read.csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv",
stringsAsFactors = FALSE, check.names = FALSE)
death_US <- death_US[,c(-1,-2,-3,-4,-5,-6,-8,-9,-10,-11,-12)]
## look at which dates you want to look at
## this removes all the dates prior to the date you want to start at
confirmed_US <- confirmed_US[,-c(2:(ncol(confirmed_US)-180))]
death_US <- death_US[,-c(2:(ncol(death_US)-180))]
library(plyr)
confirmed_US <- aggregate(. ~ Province_State, data=confirmed_US, FUN=sum)
death_US <- aggregate(. ~ Province_State, data=death_US, FUN=sum)
#########################################################################################################
confirmed_US <- confirmed_US[!(confirmed_US$Province_State=="Grand Princess" | confirmed_US$Province_State=="Diamond Princess"),]
death_US <- death_US[!(death_US$Province_State=="Grand Princess" | death_US$Province_State=="Diamond Princess"),]
populations <- data.frame(htmltab("https://en.wikipedia.org/wiki/List_of_states_and_territories_of_the_United_States_by_population", 1)[,c(3,4)], stringsAsFactors=F)
populations <- populations[!(populations$State=="Contiguous United States" |
populations$State=="The fifty states" |
populations$State=="Fifty states + D.C." |
populations$State=="Total U.S. (including D.C. and territories)"),]
colnames(populations) <- c("State", "Population")
populations[54,1] <- "Virgin Islands"
populations$Population <- destring(populations$Population)
confirmed_US <- confirmed_US[order(confirmed_US$Province_State),]
populations <- populations[order(populations$State),]; row.names(populations) <- 1:nrow(populations)
confirmed_US <- add_column(confirmed_US, Population=populations$Population, .after=1)
#########################################################################################################
daily <- function(State = st){
state <- State
confirmed_US_Subset <- subset(confirmed_US, Province_State == state)
confirmed_US_Subset <- melt(confirmed_US_Subset, id.vars = c("Province_State","Population"),
variable.name = "Date", value.name = "Confirmed")
confirmed_US_Subset$Diff[2:nrow(confirmed_US_Subset)] <- diff(confirmed_US_Subset$Confirmed)
confirmed_US_Subset$Diff[1] <- 0
confirmed_US_Subset$Date <- as.Date(as.character(confirmed_US_Subset$Date), format = c("%m/%d/%y"))
plot_ly(confirmed_US_Subset,
x=~Date, y=~Diff, type = "bar") %>%
layout(xaxis=list(title="Date", tickfont = list(size = 9)), yaxis=list(title="Cases")) %>%
layout(title = list(text = paste("Daily New Confirmed Cases for", state, sep=" "))) %>%
layout(yaxis = list(range = c(0, 1.1*(max(confirmed_US_Subset$Diff))))) %>%
config(displayModeBar = F)
}
######################################################################################33
chance <- function(State = st){
state <- State
confirmed_US_Subset <- subset(confirmed_US, Province_State == state)
confirmed_US_Subset <- melt(confirmed_US_Subset, id.vars = c("Province_State","Population"),
variable.name = "Date", value.name = "Confirmed")
death_US_Subset <- subset(death_US, Province_State == state)
death_US_Subset <- melt(death_US_Subset, id.vars = c("Province_State"),
variable.name = "Date", value.name = "Death")
confirmed_US_Subset$Diff[2:nrow(confirmed_US_Subset)] <- diff(confirmed_US_Subset$Confirmed)
confirmed_US_Subset$Diff[1] <- 0
confirmed_US_Subset$Date <- as.Date(as.character(confirmed_US_Subset$Date), format = c("%m/%d/%y"))
confirmed_US_Subset$Recovered <- ceiling(confirmed_US_Subset$Confirmed*(0.624594469))
confirmed_US_Subset$Death <- death_US_Subset$Death
confirmed_US_Subset$Active <- confirmed_US_Subset$Confirmed-confirmed_US_Subset$Recovered-confirmed_US_Subset$Death
confirmed_US_Subset$InfectionChance <- (confirmed_US_Subset$Active/(confirmed_US_Subset$Population-confirmed_US_Subset$Confirmed))*100
plot_ly(confirmed_US_Subset, x=~Date, y=~InfectionChance, type = "scatter", mode = "lines") %>%
layout(xaxis=list(title="Date", tickfont = list(size = 9)), yaxis=list(title="Chance of Infection(%)")) %>%
layout(title = list(text = paste("Daily Chances of Infection for", state, sep=" "))) %>%
layout(yaxis = list(range = c(0, 5))) %>%
config(displayModeBar = F)
}
```
# Daily Confirmed Cases in States {.tabset .tabset-fade}
Row
-------------------------------------
### Virginia
```{r, message=F, echo=FALSE, warning=FALSE}
daily("Virginia")
```
### Washington DC
```{r, message=F, echo=FALSE, warning=FALSE}
daily("District of Columbia")
```
Row
-------------------------------------
### Florida
```{r, message=F, echo=FALSE, warning=FALSE, out.width="100%", fig.align="center"}
daily("Florida")
```
### California
```{r, message=F, echo=FALSE, warning=FALSE, out.width="100%", fig.align="center"}
daily("California")
```
Row
-------------------------------------
### Texas
```{r, message=F, echo=FALSE, warning=FALSE}
daily("Texas")
```
### North Carolina
```{r, message=F, echo=FALSE, warning=FALSE}
daily("North Carolina")
```
# Chance of Infection in States {.tabset .tabset-fade}
Row
-------------------------------------
### Virginia
```{r, message=F, echo=FALSE, warning=FALSE}
chance("Virginia")
```
### Washington DC
```{r, message=F, echo=FALSE, warning=FALSE}
chance("District of Columbia")
```
Row
-------------------------------------
### Florida
```{r, message=F, echo=FALSE, warning=FALSE, out.width="100%", fig.align="center"}
chance("Florida")
```
### California
```{r, message=F, echo=FALSE, warning=FALSE, out.width="100%", fig.align="center"}
chance("California")
```
Row
-------------------------------------
### Texas
```{r, message=F, echo=FALSE, warning=FALSE}
chance("Texas")
```
### North Carolina
```{r, message=F, echo=FALSE, warning=FALSE}
chance("North Carolina")
```